Method and system for adaptively switching prediction strategies optimizing time-variant energy consumption of built environment

ABSTRACT

A computer-implemented method and system is provided. The system adaptively switches prediction strategies to optimize time-variant energy demand and consumption of built environments associated with renewable energy sources. The system analyzes a first, second, third, fourth and a fifth set of statistical data. The system derives of a set of prediction strategies for controlled and directional execution of analysis and evaluation of a set of predictions for optimum usage and operation of the plurality of energy consuming devices. The system monitors a set of factors corresponding to the set of prediction strategies and switches a prediction strategy from the set of derived prediction strategies. The system predicts a set of predictions for identification of a potential future time-variant energy demand and consumption and predicts a set of predictions. The system manipulates an operational state of the plurality of energy consuming devices and the plurality of energy storage and supply means.

CROSS-REFERENCES TO RELATED APPLICATION

The present application claims the benefit under 35 U.S.C. § 119(e) ofthe filing date of U.S. Provisional Patent Application Ser. No.62/334,367 for PHILLIP KOPP, filed May 10, 2016, which is herebyincorporated by reference in its entirety for all purposes.

TECHNICAL FIELD

The present disclosure relates to a field of energy management system.More specifically, the present disclosure relates to a method and systemfor adaptively switching prediction strategies to optimize time-variantenergy demand and consumption of one or more built environments.

BACKGROUND

Over the last few decades, increasing population and energy requirementsto power modern transportation and electronic technologies result in arapid development in energy generation and distribution technology. Inorder to meet the energy generation and distribution requirements,energy utilities depend mostly on the non-renewable energy sources likefossil fuels which produce a high amount of carbon emissions. Refinementprocesses of fossil fuels and/or its by-products and their combustion todrive electric generators have contributed as one of the major cause ofexcessive carbon emissions. The release of carbon and other chemicalby-products into the atmosphere has impacted temperatures and climatepatterns on a global scale. The increased awareness of the impacts ofcarbon emissions from the use of fossil fueled electric generation alongwith the increased cost of producing high power during peak loadconditions has increased the need for alternative solutions. Thesealternative solutions are referred to as renewable energy sources, whichmay be used to generate electricity. These renewable energy sources maybe applied to electric drive trains, electric automation andtransportation, without the need to extract, transport, refine, combust,and release carbon-based fossil fuels. Renewable energy comes in manyforms, but significantly is generated by capturing energy from natural,non-carbon intensive sources such as wind, sunlight, water movement,geothermal and other new sources as they are discovered and improved.Unlike fossil fuels and/or its by-products, these renewable energysources are complex in nature as they are intermittent and cannot becontrolled actively by humans. This enhances the probability ofoccurrence of certain time periods where power production far exceedsdemands or certain time periods where power production falls short ofdemands. This creates major challenges for energy utilities to makeinvestments, generate power for sale and profit. Also, this createsmajor challenges for the markets in establishing a price of energy forconsumers. Nowadays, energy storage means are deployed to store energywhen power production is excessive and release energy when demandsexceed power production output. These energy storage means include butmay not be limited to batteries and sophisticated power banks. However,there are many limitations to the effective installation of the energystorage means due to sizing requirements, specific load profiles andother attributes which must be matched very carefully in order toprovide feasible economic returns.

SUMMARY

In a first example, a computer-implemented method is provided. Thecomputer-implemented method adaptively switches prediction strategies tooptimize time-variant energy demand and consumption of one or more builtenvironments associated with renewable energy sources. Thecomputer-implemented method may include a first step of analysis of afirst set of statistical data, a second set of statistical data, a thirdset of statistical data, a fourth set of statistical data and a fifthset of statistical data. The first set of statistical data may beassociated with a plurality of energy consuming devices. The second setof statistical data may be associated with an occupancy behavior of aplurality of users. The third set of statistical data may be associatedwith a plurality of energy storage and supply means. The fourth set ofstatistical data may be associated with a plurality of environmentalsensors and the fifth set of statistical data may be associated with aplurality of energy pricing models. In addition, thecomputer-implemented method may include a second step of derivation of aset of prediction strategies. The set of prediction strategies enablecontrolled and directional execution of analysis and evaluation of a setof predictions for optimum usage and operation of the plurality ofenergy consuming devices. Moreover, the computer-implemented method mayinclude a third step to monitor a set of factors corresponding to theset of prediction strategies discretely indicating prominence of the setof prediction strategies. Further, the computer-implemented method mayinclude a fourth step to switch to a prediction strategy from the set ofderived prediction strategies. Furthermore, the computer-implementedmethod may include a fifth step of prediction of a set of predictionsfor identifying a potential future time-variant energy demand andconsumption associated with the one or more built environments. Also,the computer-implemented method may include a fifth step of predictionof a set of predictions for identification of a potential futuretime-variant energy demand and consumption associated with the one ormore built environments. In addition, the computer-implemented methodmay include a sixth step of manipulation an operational state of theplurality of energy consuming devices and the plurality of energystorage and supply means. The analysis may be done by performing one ormore statistical functions to generate a plurality of statisticalresults. The switching may be performed based on selective prominence ofone or more factors in the monitored set of factors corresponding toswitched predication strategy of the one or more prediction strategies.The manipulation may be performed based on the set of predictions. Theoperational state of the plurality of energy consuming devices may bemanipulated by time-variant shifting and scheduling of operation of eachof the selected energy consuming device of the plurality of energyconsuming devices in a scheduled usage profile of the selected energyconsuming device of the plurality of energy consuming devices. Thetime-variant shifting and scheduling may be performed for regulation ofthe usage profile and rendering steeper energy demand curves for the oneor more built environments. In addition, the manipulation may be done byintegration of energy storage and supply means for optimally reducing apeak level of energy demand concentrated over a limited period of time.The integration is done based on validation of an increase in the energydemand above a threshold level. The energy storage and supply means maybe integrated for providing an energy demand reduction from an energystorage device with lower energy storage capacity. The demand reductionfrom the energy storage device provides a potential for optimumdischarge over a period of time.

In a second example, a computer system is provided. The computer systemmay include one or more processors and a memory coupled to the one ormore processors. The memory may store instructions which, when executedby the one or more processors, may cause the one or more processors toperform a method. The method adaptively switches prediction strategiesto optimize time-variant energy demand and consumption of one or morebuilt environments associated with renewable energy sources. The methodmay include a first step of analysis of a first set of statistical data,a second set of statistical data, a third set of statistical data, afourth set of statistical data and a fifth set of statistical data. Thefirst set of statistical data may be associated with a plurality ofenergy consuming devices. The second set of statistical data may beassociated with an occupancy behavior of a plurality of users. The thirdset of statistical data may be associated with a plurality of energystorage and supply means. The fourth set of statistical data may beassociated with a plurality of environmental sensors and the fifth setof statistical data may be associated with a plurality of energy pricingmodels. In addition, the method may include a second step of derivationof a set of prediction strategies. The set of prediction strategiesenable controlled and directional execution of analysis and evaluationof a set of predictions for optimum usage and operation of the pluralityof energy consuming devices. Moreover, the method may include a thirdstep to monitor a set of factors corresponding to the set of predictionstrategies discretely indicating prominence of the set of predictionstrategies. Further, the method may include a fourth step to switch to aprediction strategy from the set of derived prediction strategies.Furthermore, the method may include a fifth step of prediction of a setof predictions for identifying a potential future time-variant energydemand and consumption associated with the one or more builtenvironments. Also, the method may include a fifth step of prediction ofa set of predictions for identification of a potential futuretime-variant energy demand and consumption associated with the one ormore built environments. In addition, the method may include a sixthstep of manipulation of an operational state of the plurality of energyconsuming devices and the plurality of energy storage and supply means.The analysis may be done by performing one or more statistical functionsto generate a plurality of statistical results. The switching may beperformed based on selective prominence of one or more factors in themonitored set of factors corresponding to switched predication strategyof the one or more prediction strategies. The manipulation may beperformed based on the set of predictions. The operational state of theplurality of energy consuming devices may be manipulated by time-variantshifting and scheduling of operation of each of the selected energyconsuming device of the plurality of energy consuming devices in ascheduled usage profile of the selected energy consuming device of theplurality of energy consuming devices. The time-variant shifting andscheduling may be performed for regulation of the usage profile andrendering steeper energy demand curves for the one or more builtenvironments. In addition, the manipulation may be done by integrationof energy storage and supply means for optimally reducing a peak levelof energy demand concentrated over a limited period of time. Theintegration is done based on validation of an increase in the energydemand above a threshold level. The energy storage and supply means maybe integrated for providing an energy demand reduction from an energystorage device with lower energy storage capacity. The demand reductionfrom the energy storage device provides a potential for optimumdischarge over a period of time.

In a third example, a computer-readable storage medium is provided. Thecomputer-readable storage medium encodes computer executableinstructions that, when executed by at least one processor, performs amethod. The method adaptively switches prediction strategies to optimizetime-variant energy demand and consumption of one or more builtenvironments associated with renewable energy sources. The method mayinclude a first step of analysis of a first set of statistical data, asecond set of statistical data, a third set of statistical data, afourth set of statistical data and a fifth set of statistical data. Thefirst set of statistical data may be associated with a plurality ofenergy consuming devices. The second set of statistical data may beassociated with an occupancy behavior of a plurality of users. The thirdset of statistical data may be associated with a plurality of energystorage and supply means. The fourth set of statistical data may beassociated with a plurality of environmental sensors and the fifth setof statistical data may be associated with a plurality of energy pricingmodels. In addition, the method may include a second step of derivationof a set of prediction strategies. The set of prediction strategiesenable controlled and directional execution of analysis and evaluationof a set of predictions for optimum usage and operation of the pluralityof energy consuming devices. Moreover, the method may include a thirdstep to monitor a set of factors corresponding to the set of predictionstrategies discretely indicating prominence of the set of predictionstrategies. Further, the method may include a fourth step to switch to aprediction strategy from the set of derived prediction strategies.Furthermore, the method may include a fifth step of prediction of a setof predictions for identifying a potential future time-variant energydemand and consumption associated with the one or more builtenvironments. Also, the method may include a fifth step of prediction ofa set of predictions for identification of a potential futuretime-variant energy demand and consumption associated with the one ormore built environments. In addition, the method may include a sixthstep of manipulation of an operational state of the plurality of energyconsuming devices and the plurality of energy storage and supply means.The analysis may be done by performing one or more statistical functionsto generate a plurality of statistical results. The switching may beperformed based on selective prominence of one or more factors in themonitored set of factors corresponding to switched predication strategyof the one or more prediction strategies. The manipulation may beperformed based on the set of predictions. The operational state of theplurality of energy consuming devices may be manipulated by time-variantshifting and scheduling of operation of each of the selected energyconsuming device of the plurality of energy consuming devices in ascheduled usage profile of the selected energy consuming device of theplurality of energy consuming devices. The time-variant shifting andscheduling may be performed for regulation of the usage profile andrendering steeper energy demand curves for the one or more builtenvironments. In addition, the manipulation may be done by integrationof energy storage and supply means for optimally reducing a peak levelof energy demand concentrated over a limited period of time. Theintegration is done based on validation of an increase in the energydemand above a threshold level. The energy storage and supply means maybe integrated for providing an energy demand reduction from an energystorage device with lower energy storage capacity. The demand reductionfrom the energy storage device provides a potential for optimumdischarge over a period of time.

BRIEF DESCRIPTION OF THE FIGURES

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 illustrates an interactive environment for adaptively switchingprediction strategies to optimize time-variant energy demand andconsumption of one or more built environments, in accordance withvarious embodiments of the present disclosure;

FIG. 2 illustrates a block diagram for adaptively switching predictionstrategies to optimize time-variant energy demand and consumption of oneor more built environments, in accordance with various embodiments ofthe present disclosure;

FIG. 3 illustrates a block diagram of an energy demand control system,in accordance with various embodiments of the present disclosure;

FIG. 4 illustrates a flow chart for adaptively switching predictionstrategies to optimize time-variant energy demand and consumption of oneor more built environments, in accordance with various embodiments ofthe present disclosure; and

FIG. 5 illustrates a block diagram of a communication device, inaccordance with various embodiments of the present disclosure.

It should be noted that the accompanying figures are intended to presentillustrations of exemplary embodiments of the present disclosure. Thesefigures are not intended to limit the scope of the present disclosure.It should also be noted that accompanying figures are not necessarilydrawn to scale.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present technology. It will be apparent, however,to one skilled in the art that the present technology can be practicedwithout these specific details. In other instances, structures anddevices are shown in block diagram form only in order to avoid obscuringthe present technology.

Reference in this specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the present technology. The appearance of the phrase “in oneembodiment” in various places in the specification are not necessarilyall referring to the same embodiment, nor are separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, variousfeatures are described which may be exhibited by some embodiments andnot by others. Similarly, various requirements are described which maybe requirements for some embodiments but not other embodiments.

Moreover, although the following description contains many specifics forthe purposes of illustration, anyone skilled in the art will appreciatethat many variations and/or alterations to said details are within thescope of the present technology. Similarly, although many of thefeatures of the present technology are described in terms of each other,or in conjunction with each other, one skilled in the art willappreciate that many of these features can be provided independently ofother features. Accordingly, this description of the present technologyis set forth without any loss of generality to, and without imposinglimitations upon, the present technology.

FIG. 1 illustrates an interactive environment for adaptively switchingprediction strategies to optimize time-variant energy demand andconsumption associated with one or more built environments, inaccordance with various embodiment of the present disclosure. Theinteractive environment facilitates assimilation and analysis of energyconditions associated with the one or more built environments. Theenergy conditions include but may not be limited to energy demand,energy consumption, energy expenses and energy use intensity. Theanalyzed energy conditions are utilized for deriving and switchingprediction strategies for precisely predicting a set of predictionsassociated with the potential future time-variant energy demand andconsumption. In addition, the set of predictions are utilized toaccurately manipulate the energy demand and consumption renderingsteeper energy demand curves to optimize the time-variant energy demandand consumption.

The interactive environment is characterized by the interaction of abuilt environment 102, a plurality of energy consuming devices 104, aplurality of energy storage and supply means 106, a plurality of sensors108 and one or more data collecting devices 110. In addition, theinteractive environment is characterized by the interaction of acommercial power supply grid node 112, one or more renewable energysupply sources 114 and a communication network 116. Furthermore, theinteractive environment is characterized by the interaction of aplurality of environmental sensors 118 and a plurality of energy pricingmodels 120. Moreover, the interactive environment is characterized bythe interaction of an energy demand control system 122, a plurality ofexternal application program interfaces 124 (hereafter “APIs”) and oneor more statistical monitoring devices 126.

In general, the built environment 102 is a closed or semi-closedstructure with one or more number of floors utilized for specificpurposes. Each built environment are utilized to perform a pre-definedoperations and maintenance based on types of services provided by thebuilt environment 102. The types of services include hospitality,travel, work, entertainment, manufacturing and the like. In addition,each type of service provided decides a scale of the operations andmaintenance of the built environment 102. The type of services and themaintenance pertains to the energy consumption associated with each ofthe plurality of energy consuming devices 104. Examples of the builtenvironment 102 include but may not be limited to an office, a mall, anairport, a stadium, a hotel and a manufacturing plant.

The built environment 102 utilizes energy for operations and maintenanceof the built environment 102. The built environment 102 obtains theenergy from a plurality of energy generation and supply sources. Theplurality of energy generation and supply sources include but may not belimited to the commercial power supply grid node 112 and the one or morerenewable energy supply sources 114. The commercial power supply gridnode 112 corresponds to a network of power lines, a plurality oftransformers and one or more equipment employed for the transmission anddistribution of the alternate current power to the built environment102. Further, the one or more renewable energy supply sources 114include but may not be limited to one or more windmills and pluralitysolar photovoltaic panels.

In an embodiment of the present disclosure, the one or more renewableenergy supply sources 114 are deployed at the built environment 102. Inan example, the plurality of solar photovoltaic panels is installed atthe residential or commercial rooftops. In another embodiment of thepresent disclosure, the one or more renewable energy supply sources 114are deployed at a remote location from the built environment 102. In anexample, the one or more windmills are deployed at countryside farmland.In an embodiment of the present disclosure, the one or more renewableenergy supply sources 114 is directly connected to the plurality ofenergy storage and supply means 106 associated with the builtenvironment 102. The one or more renewable energy supply sources 114directly provides DC energy to the plurality of energy storage andsupply means 106 without going through any voltage or current conversionprocess. In another embodiment of the present disclosure, the one ormore renewable energy supply sources 114 is connected with the builtenvironment 102 to supply available energy through the use of a directcurrent to alternating current inverter.

The plurality of energy storage and supply means 106 is configured tostore the energy and supply to fulfill energy demand associated with thebuilt environment 102. In an embodiment of the present disclosure, theplurality of energy storage and supply means 106 includes one or morebattery cells assembled to create one or more battery packs capable ofcharging and discharging electric energy. In another embodiment of thepresent disclosure, the plurality of energy storage and supply means 106is a high speed flywheel energy storage means. In yet another embodimentof the present disclosure, the plurality of energy storage and supplymeans 106 is pumped hydro energy storage means.

In yet another embodiment of the present disclosure, the plurality ofenergy storage and supply means 106 is non-electrical energy storingmediums. In an example, the energy storage and supply means may becomprised of thermal mass or momentum, such that the calculated amountof energy as converted to heat is stored within the energy storage andsupply means. In addition, the energy is stored and released at acertain rate using heat transfer or pumping as an energy transfermedium. In another example, a building environment, its construction,envelope and contents are utilized as a means to store, transfer andrelease energy passively or actively in a form of heat when combinedwith a means of artificial heating and cooling.

In an embodiment of the present disclosure, the plurality of energystorage and supply means 106 is located at a central location in thebuilt environment 102. The central location associated with the builtenvironment 102 includes an electrical room or closet, exterior in aspecialized storage cabinet or container and the like. In anotherembodiment of the present disclosure, the plurality of energy storageand supply means 106 is co-located with each of the plurality of energyconsuming devices 104. In yet another embodiment of the presentdisclosure, the plurality of energy storage and supply means 106 isdistributed throughout the built environment 102. In an example, theplurality of energy storage and supply means 106 is distributed instand-alone forms, plug-in forms and design oriented forms such asfurniture or permanent wall hanging forms or picture frames.

In yet another embodiment of the present disclosure, the plurality ofenergy storage and supply means 106 is built into the building structureor building electrical distribution itself. In yet another embodiment ofthe present disclosure, the plurality of energy storage and supply means106 is in a form of thermal heat mass capture and release usingcalculated capacities of building materials. In yet another embodimentof the present disclosure, the plurality of energy storage and supplymeans 106 is located outside of the built environment 102 in amicro-grid or fractal grid application.

Going further, the built environment 102 is associated with a pluralityof users 128 present inside the built environment 102. The plurality ofusers 128 may be any human operator, human worker, occupants, datamanager, visitors and the like. Each of the plurality of users 128 isassociated with a task. For example, the human operators perform thetask of monitoring and regulating machines. In another example, thehuman workers perform the task of cleaning, sweeping and repairing. Inyet another example, the occupants are the employees that includemanagers, attendants, assistants, clerk, security staff and the like. Inyet another example, the visitors are civilians present for a specificperiod of time.

Each of the plurality of users 128 utilizes a pre-defined amount of theenergy. The pre-defined amount of the energy pertains to a correspondingenergy consuming device of the plurality of energy consuming devices104. Moreover, each of the plurality of energy consuming devices 104performs an operation to meet requirements of the plurality ofoperations associated with the built environment 102. The plurality ofoperations is associated with operation of each of the plurality ofenergy consuming devices 104 installed in the built environment 102. Theplurality of energy consuming devices 104 may be of any type and size.In addition, the plurality of energy consuming devices 104 includes aplurality of electrical devices and a plurality of portablecommunication devices.

In an embodiment of the present disclosure, the plurality of energyconsuming devices 104 may have any electrical and mechanicalapplications. Examples of the plurality of energy consuming devices 104include but may not be limited to lighting circuits, refrigerationunits, air conditioning systems, information technology networks, gasboilers, hot water heater, escalators, and elevators. The plurality ofenergy consuming devices 104 consumes a pre-defined amount of the energybased on a power rating, duration of energy usage and the plurality ofoperations performed. The pre-defined amount of the energy consumed bythe plurality of energy consuming devices 104 is based on one or moreenergy physical variables. The one or more energy physical variablesinclude but may not be limited to a power factor, a phase angle, a powerfrequency, a voltage, a current load and a power demand.

The one or more energy physical variables of each of the plurality ofenergy consuming devices 104 is monitored and measured by a plurality ofenergy metering devices. Each of the plurality of energy consumingdevices 104 is combined with the plurality of energy metering devices.In an embodiment of the present disclosure, the plurality of energymetering devices is installed inside each of the plurality of energyconsuming devices 104. The plurality of energy metering devices measureseach of the one or more energy physical variables in real time. Theplurality of energy metering devices include but may not be limited todigital multi-meters, current sensors and wattage meters. In addition,the plurality of energy metering devices facilitates collection of afirst set of statistical data associated with the plurality of energyconsuming devices 104.

The collection of the first set of statistical data uses a method. In anembodiment of the present disclosure, the method involves digitalcollection of the first set of statistical data for each of theplurality of energy consuming devices 104. In another embodiment of thepresent disclosure, the method involves physical collection of the firstset of statistical data for each of the plurality of energy consumingdevices 104. The plurality of energy metering devices monitors a firstplurality of parameters. The first plurality of parameters is associatedwith the plurality of energy consuming devices 104. The first pluralityof parameters includes a set of operational characteristics and a set ofphysical characteristics. The set of operational characteristics includea current rating, a voltage rating, a power rating, a frequency ofoperation, an operating temperature and a device temperature. Inaddition, the set of operational characteristics include duration of theenergy usage by each of the plurality of energy consuming devices 104 inthe built environment 102. Moreover, the set of operationalcharacteristics include a seasonal variation in operation and anoff-seasonal variation in operation. Further, the set of physicalcharacteristics include a device size, a device area, a device physicallocation and a portability of device. In an embodiment of the presentdisclosure, the one or more energy metering devices collects the firstset of statistical data. In addition, the first set of statistical dataincludes a current operational state data and a past operational statedata. The current operational state data and the past operational statedata corresponds to current energy consumption data and the historicalenergy consumption data associated with the plurality of energyconsuming devices 104 of the built environment 102.

Going further, the plurality of energy consuming devices 104 isassociated with the plurality of users 128. The plurality of users 128interacts with the plurality of energy consuming devices 104 installedin the built environment 102 to perform specific operations. The dailyusage and the operating characteristics of the plurality of energyconsuming devices 104 are derived from an interface associated with eachuser of the plurality of users 128. Each of the plurality of energyconsuming devices 104 consumes a pre-defined amount of energy during theinterface. The pre-defined amount of energy is derived based on anenergy consumption behavior and an occupancy pattern of each of theplurality of users 128. In an example, each user of the plurality ofusers 128 in the built environment 102 may arrive and leave the builtenvironment 102 during certain hours each day. Each user carries one ormore portable communication devices both in and out of the builtenvironment 102.

Further, the energy consumption behavior and the occupancy pattern isrecorded for each of the plurality of users 128 to obtain a second setof statistical data. The energy consumption behavior and occupancypattern is collected and recorded by a plurality of occupancy detectionmeans. The plurality of occupancy detection means collect the energyconsumption behavior and occupancy pattern associated with the pluralityof users 128 in real time. The plurality of occupancy detection meansare installed inside and outside of the built environment 102. Theplurality of occupancy detection means include a plurality of occupancysensing devices. The plurality of occupancy sensing devices includeoccupancy sensors, door state sensors, motion detectors, microphones,radio frequency identification (hereinafter as “RFID”), radio receivedsignal strength indicators (hereinafter as “RSSI”) and digital or radiofrequency signal processors. Furthermore, the plurality of occupancydetection means include the plurality of sensors 108. The plurality ofsensors 108 include carbon-monoxide sensors, carbon-dioxide sensors,heat sensors, pressure sensors, atmospheric pressure sensors,temperature sensors, energy flow sensors, energy fingerprint sensors onmonitored loads physical touch point sensors and the like.

The first set of statistical data and the second set of statistical datais transferred to the one or more data collecting devices 110 associatedwith the built environment 102. The one or more data collecting devices110 collects the first set of statistical data and the second set ofstatistical data. The one or more data collecting devices 110 performdigital collection and manual collection. In an embodiment of thepresent disclosure, each of the one or more data collecting devices 110is a portable device with an inbuilt API. The inbuilt API of each of theone or more data collection devices 110 is associated with a GlobalPositioning system (hereafter “GPS”). Further, the inbuilt API of eachof the one or more data collection devices 110 is associated with acamera and keypad designed for manual data input from the plurality ofusers 128. In another embodiment of the present disclosure, each of theone or more data collecting devices 110 is a cellular modem. In yetanother embodiment of the present disclosure, each of the one or moredata collecting devices 110 is any suitable data gateway device.

The one or more data collecting devices 110 collects a third set ofstatistical data associated with each of the plurality of energy storageand supply means 106. In an embodiment of the present disclosure, theone or more data collecting devices 110 receives the third set ofstatistical data from the plurality of energy monitoring devicesassociated with each of the plurality of energy storage and supply means106. The plurality of energy monitoring devices monitor a secondplurality of parameters associated with the plurality of energy storageand supply means 106. In addition, the plurality of energy monitoringdevices collect and transfer the second plurality of parametersassociated with the plurality of energy storage and supply means 106 tothe one or more data collecting devices 110 in real time. The secondplurality of parameters include but may not be limited to charging anddischarging rates, temperature characteristics, an energy storage andrelease capacity associated with the plurality of energy storage.

The one or more data collecting devices 110 is associated with thecommunication network 116 through an internet connection. The internetconnection is established based on a type of network. In an embodimentof the present disclosure, the type of network is a wireless mobilenetwork. In another embodiment of the present disclosure, the type ofnetwork is a wired network with a finite bandwidth. In yet anotherembodiment of the present disclosure, the type of network is acombination of the wireless and the wired network for the optimumthroughput of data transmission. The communication network 116 includesa set of channels with each channel of the set of channels supporting afinite bandwidth. The finite bandwidth of each channel of the set ofchannels is based on a capacity of the communication network 116. Thecommunication network 116 transmits a pre-defined size of the first setof statistical data, the second set of statistical data and the thirdset of statistical data to the energy demand control system 122. Thepre-defined size corresponding to the first set of statistical data, thesecond set of statistical data and the third set of statistical data ismeasured in terms of at least one of bits, bytes, kilobytes, megabytes,gigabytes, terabytes and petabytes. Accordingly, the energy demandcontrol system 122 receives the pre-defined size of the first set ofstatistical data, the second set of statistical data and the third setof statistical data. In addition, the energy demand control system 122receives another part of the first set of statistical data, the secondset of statistical data and the third set of statistical data from theplurality of external APIs 124 and third party databases.

Continuing with FIG. 1, the energy demand control system 122 receives afourth set of statistical data and a fifth set of statistical data. Theenergy demand control system 122 receives the fourth set of statisticaldata from the plurality of environmental sensors 118 through thecommunication network 116. The plurality of environmental sensors 118detect and collect environmental and weather conditions associated withthe built environment 102 in real time. In addition, the plurality ofenvironmental sensors 118 transfer the environmental and weatherconditions to the energy demand control system 122 in real time. In anembodiment of the present disclosure, the plurality of environmentalsensors 118 is present inside the built environment 102. In anotherembodiment of the present disclosure, the plurality of environmentalsensors 118 is present outside the built environment 102. Further, theenergy demand control system 122 receives the fifth set of statisticaldata from the plurality of energy pricing models 120. The plurality ofenergy pricing models 120 is configured to record energy pricesassociated with the built environment 102.

The energy demand control system 122 receives another part of the fourthset of statistical data and the fifth set of statistical data from theplurality of external APIs 124 and third party databases. The pluralityof external APIs 124 and the third party databases are configured tocollect, store and transmit weather history and weather forecasts. Inaddition, the plurality of external APIs 124 and the third partydatabases are configured to collect, store and transmit billing data, apast energy consumption data and metered energy data. Furthermore, theplurality of external APIs 124 and the third party databases areconfigured to collect, store and transmit financial or non-financialbusiness data. The financial or non-financial business data comes frombusiness management software. Example of the business managementsoftware includes Enterprise Resources Planning (ERP) software.

The energy demand control system 122 analyzes the first set ofstatistical data, the second set of statistical data, the third set ofstatistical data, the fourth set of statistical data and the fifth setof statistical data. The analysis is done by performing one or morestatistical functions (discussed below in detailed description of FIG.2). The energy demand control system 122 performs the one or morestatistical functions to generate a plurality of statistical results.The plurality of statistical results pertains to the energy consumption(discussed below in detailed description of FIG. 2). The plurality ofstatistical results obtained from the analysis is used as a referencebasis of the energy consumption for deriving a set of predictionstrategies (discuss in detailed description of FIG. 2). Further, theenergy demand control system 122 switch to a prediction strategy fromthe set of prediction strategies based on a selective prominence of oneor more factors (explained in detailed description of FIG. 1). Theswitching is performed to precisely predict and manipulate thetime-variant energy demand and consumption of the built environment 102

Further, the energy demand control system 122 displays the plurality ofstatistical results through an application installed in a mobile phone,tablet, smart watch and the like. In another embodiment of the presentdisclosure, the energy demand control system 122 displays each of theplurality of statistical results on a web page. In yet anotherembodiment of the present disclosure, the energy demand control system122 displays each of the plurality of statistical results on a pluralityof monitors. Furthermore, the energy demand control system 122 executesthe one or more control schemes for controlling the operational statesof each of the plurality of energy consuming devices 104 and theplurality of energy storage and supply means 106. The one or morecontrol schemes are executed based on the set of predictions (explainedbelow in the detailed description of the FIG. 2).

Further, the energy demand control system 122 transfers the plurality ofstatistical results along with the set of predictions and the one ormore control schemes to the one or more statistical monitoring devices126. The one or more statistical monitoring devices 126 is configured toreceive and display at least one of the first set of statistical data,the second set of statistical data, the third set of statistical data,the fourth set of statistical data and the fifth set of statisticaldata. In addition, the one or more statistical monitoring devices 126are configured to receive and display at least one of the plurality ofstatistical results, the set of predictions and the one or more controlschemes for precise monitoring and manipulation. The one or morestatistical monitoring devices 126 is a device capable of receiving thefirst set of statistical data, the second set of statistical data, thethird set of statistical data, the fourth set of statistical data andthe fifth set of statistical data from the energy demand control system122. Also, the one or more statistical monitoring devices 126 is adevice capable of receiving the plurality of statistical results, theset of predictions and the one or more control schemes from the energydemand control system 122.

It may be noted that in FIG. 1, the energy demand control system 122transfers the first set of statistical data, the second set ofstatistical data, the third set of statistical data, the fourth set ofstatistical data, the fifth set of statistical data, the plurality ofstatistical results, the set of predictions and the one or more controlschemes to the one or more statistical monitoring devices 126; however,those skilled in the art would appreciate that the energy demand controlsystem 122 transfers the first set of statistical data, the second setof statistical data, the third set of statistical data, the fourth setof statistical data, the fifth set of statistical data, the plurality ofstatistical results, the set of predictions and the one or more controlschemes to more number of statistical monitoring devices. Furthermore,it may be noted that in FIG. 1, the built environment 102 is connectedto the energy demand control system 122 through the communicationnetwork 116; however, those skilled in the art would appreciate thatmore number of built environments are connected to the energy demandcontrol system 122 through the communication network 116.

FIG. 2 illustrates a block diagram 200 for adaptively switchingprediction strategies to optimize time-variant energy demand andconsumption of the built environment 102, in accordance with variousembodiments of the present disclosure. It may be noted that to explainthe system elements of FIG. 2, references will be made to the systemelements of the FIG. 1.

The block diagram 200 includes the built environment 102, commercialpower supply grid node 112, the one or more renewable energy supplysources 114, the energy demand control system 122 and the plurality ofexternal APIs 124 (as discussed above in detailed description of FIG.1). In addition, the block diagram 200 includes the plurality ofenvironmental sensors 118 and the plurality of energy pricing models 120and the one or more statistical monitoring devices 126 (as discussedabove in detailed description of FIG. 1). Moreover, the block diagram200 includes a network based automatic control system 202 and thirdparty databases 206. Furthermore, the energy demand control system 122includes a server 204. In addition, the server 204 includes a database204 a and a processor 204 b.

Each of the plurality of energy consuming devices 104 is associated withone or more energy physical variables (as described above in detaileddescription of FIG. 1). The one or more energy physical variablesdefines the energy consumption in the real time based on the load. In anembodiment of the present disclosure, each of the plurality of energyconsuming devices 104 is associated with the plurality of energymetering devices. The plurality of energy metering devices digitallymeasures one or more energy physical variables in the real time toobtain the first set of statistical data (as discussed above in detaileddescription of FIG. 1). The plurality of energy metering devicesincludes one or more digital meters, one or more digital current andvoltage sensors, the multi-meters, watt-meters, supervisory control anddata acquisition (SCADA) and the like.

The energy demand control system 122 collects the first set ofstatistical data associated with the plurality of energy consumingdevices 104 from the plurality of energy metering devices. The first setof statistical data includes the current operational state dataassociated with the plurality of energy consuming devices 104 and thepast operational state data associated with the plurality of energyconsuming devices 104. The operational state data is associated with thepre-defined amount of energy consume by each of the plurality of energyconsuming devices 104 in real time. The plurality of energy consumingdevices 104 consumes the pre-defined amount of energy to perform aspecific operation (as discussed above in detailed description of FIG.1).

Further, the energy demand control system 122 fetches the second set ofstatistical data associated with an occupancy behavior of the pluralityof users 128 present inside each of the built environment 102. Theenergy demand control system 122 fetches the second set of statisticaldata from the plurality of occupancy detection means. The second set ofstatistical data includes a first plurality of occupancy data and asecond plurality of occupancy data. The first plurality of occupancydata is associated with energy consumption behavior of each of theplurality of users 128 present inside the built environment 102. Thesecond plurality of occupancy data is associated with the occupancypattern of each of the plurality of users 128 present inside the builtenvironment 102.

The first plurality of occupancy data is associated with interactionbetween the plurality of energy consuming devices 104 and the pluralityof users 128. In an example, a person X check-in to a hotel A. Theperson X uses the elevator to go upstairs, unlock the room by digitalcard swap and turns on the lighting and air conditioning unit. Theinteraction of the person X with the elevator, the digital card swapsdoor, the lightings and the air conditioning unit results in thepre-defined load consumption. The plurality of users 128 consumes thepre-defined amount of energy associated with the built environment 102.

The second plurality of occupancy data is associated with the occupancypattern of the plurality of users 128. The occupancy pattern of theplurality of users 128 varies with time, location, weather, season andthe like. The occupancy pattern of the plurality of users 128 varieswith different zones of the built environment 102. In an example, theoccupancy pattern of the plurality of users 128 in shopping mallsincreases during the festive seasons. In another example, the occupancypattern at the rugby ground increases during the match day.

The energy consumption behavior and occupancy pattern is recorded andcounted by the plurality of occupancy detection means to obtain thesecond set of statistical data (as described above in detaileddescription of FIG. 1). In addition, the plurality of occupancydetection means record and count based on the one or morespecifications. The one or more specifications include heat signature,identification cards, Bluetooth and the like. In an example, the recordof first time visitors and frequent visitors is maintained for fastercollection of the second set of statistical data. Further, the energyusage pattern of each of the plurality of users 128 creates a unique andaggregated consumption of the energy. The unique and aggregatedconsumption of the energy is based on a variation in number of theplurality of users 128. The variation in the number of the plurality ofusers 128 is based on days, months, seasons, events and time of year. Inaddition, the variation in the number of the plurality of users 128 maybe based on architectural configurations of the built environment 102.In an example, the occupancy pattern of the plurality of users 128 inshopping malls increases during the festive seasons. In another example,the occupancy pattern at the soccer ground increases during the matchday.

Further, the energy demand control system 122 accumulates the third setof statistical data associated with each of the plurality of energystorage and supply means 106 from the plurality of energy monitoringdevices. The third set of statistical data includes a current andhistorical energy storage and supply capacity data associated with theplurality of energy storage and supply means 106. The plurality ofenergy monitoring devices record and collect energy storage and supplycapacity data associated with the plurality of energy storage and supplymeans 106 to obtain the third set of statistical data. The energy demandcontrol system 122 accumulates the third set of statistical data basedon the second plurality of parameters (as mentioned above in detaileddescription of FIG. 1).

The energy demand control system 122 receives the fourth set ofstatistical data from the plurality of environmental sensors 118associated with the built environment 102 (discussed above in detaileddescription of FIG. 1). In addition, the energy demand control system122 receives the fourth set of statistical data from the plurality ofexternal APIs 124 and the third party databases 206. The fourth set ofstatistical data includes the current and historical environmentalcondition data of at least one of inside and outside of the builtenvironment 102. The fourth set of statistical data is received by theenergy demand control system 122 based on a third plurality ofparameters. In an embodiment of the present disclosure, the thirdplurality of parameters include but may not be limited to a means ofrecording environmental data having temperature, humidity and airpressure associated with each of the plurality of environmental sensors.

Further, the energy demand control system 122 gathers the fifth set ofstatistical data from the plurality of energy pricing models 120. Thefifth set of statistical data includes current and historical recordingsof energy pricing state affecting the built environment 102. Theplurality of energy pricing models 120 record and transfer the energypricing to the energy demand control system 122 in real time through thecommunication network 116. In addition, the energy demand control system122 gathers the fifth set of statistical data from the plurality ofexternal APIs 124 and the third party databases 206 (as discussed abovein detailed description of FIG. 1). The fifth set of statistical data isgathered based on a fourth plurality of parameters. In an embodiment ofthe present disclosure, the fourth plurality of parameters include ameans of recording energy pricing data having an energy pricing model,an energy price signal associated with the built environment 102.

Going further, the energy demand control system 122 performs theanalysis of the first set of statistical data, the second set ofstatistical data, the third set of statistical data, the fourth set ofstatistical data and the fifth set of statistical data. The energydemand control system 122 performs the one or more statistical functionsto generate the plurality of statistical results. The one or morestatistical functions include translating the current operational statedata and the past operational state data associated with the pluralityof energy consuming devices 104 into energy demand values. In addition,the one or more statistical functions include parsing the first set ofstatistical data, the second set of statistical data and the third setof statistical data. The energy demand control system 122 develops anenergy usage profile. The energy demand control system 122 develops theenergy usage profile of each of the plurality of energy consumingdevices 104, each of the plurality of energy storage and supply means106 and each of the plurality of users 128. In addition, the energydemand control system 122 develops the energy usage profile associatedwith each zone of the floor, each group of zones of floor, each floor ofa building and each of the one or more built environments.

Further, the one or more statistical functions include imputing one ormore data entries in the first set of statistical data, the second setof statistical data and the third set of statistical data. The imputingof the one or more data entries is performed to minimize errors inderiving the energy consumption and demand associated with the builtenvironment 102 for a given time interval. Moreover, the energy demandcontrol system 122 imputes the one or more data entries by using anapplication of at least one of the statistical regression, interpolationand extrapolation.

The one or more statistical functions include comparing the currentoperational state data with the past operational state data. The energydemand control system 122 compares the current operational state datawith the past operational state data associated with the each of theplurality of energy consuming devices 104. The current operational statedata and the past operational state data are compared to determine thepotential for improvement in energy consumption of each of the pluralityof energy consuming devices 104. In addition, the energy demand controlsystem 122 compares a current energy storage capacity and a past energystorage capacity associated with each of the plurality of energy storageand supply means 106. The current energy storage capacity and the pastenergy storage capacity are compared to determine the potential forimprovement in charge/discharge cycles and energy storage capacity ofeach of the plurality of energy storage and supply means 106.

Accordingly, the analysis of the first set of statistical data, thesecond set of statistical data, the third set of statistical data, thefourth set of statistical data and the fifth set of statistical dataprovides the plurality of statistical results. The plurality ofstatistical results pertains to the energy consumption. In addition, theplurality of statistical results is based on a statistical data model.The statistical data model provides a complete insight into the energyconsumption trend. The plurality of statistical results includes one ormore graphs, one or more charts, one or more tables and one or morestatistical maps of energy consumption. The plurality of statisticalresults are obtained as a function of duration of the operations of theplurality of energy consuming devices 104 and energy storage and supplycapacity of the plurality of energy storage and supply means. Inaddition, the plurality of statistical results are obtained as afunction of environmental conditions, and energy pricing affecting thebuilt environment 102.

In an example, the plurality of statistical results include a table andchart of monthly energy consumption of the built environment 102 and atable of a total monthly variable energy load. In another example, theplurality of statistical results includes a pie chart to show aseparation of the energy use in the built environment 102 and a table ofenergy consumption per month and air conditioner loads. In yet anotherexample, the plurality of statistical results includes a statisticalchart depicting a kWh consumption based on load type, a bar graph ofexpected air conditioner savings and service costs. In yet anotherexample, the plurality of statistical results include a bar chart ofgross rental, service and licensing costs of at least one of airconditioning units, air conditioning control means, statistical softwareand networks. In yet another example, the plurality of statisticalresults includes the statistical chart of total kWh consumed per room asa function of cold degree days.

Further, the energy demand control system 122 derives the set ofprediction strategies. The set of prediction strategies are utilized foridentifying the future time-variant energy demand and consumptionassociated with the built environment 102. The energy demand controlsystem 122 derives the set of prediction strategies based on theplurality of statistical results. The set of prediction strategies arederived for driving controlled and directional execution of analysis ofthe set of predictions for optimum usage and operation of the pluralityof energy consuming devices 104. In addition, the set of predictionstrategies are derived for driving controlled and directional executionof the one or more control schemes for optimum usage and operation ofthe plurality of energy consuming devices 104.

The energy demand control system 122 derives a first prediction strategyof the set of prediction strategies. In an embodiment of the presentdisclosure, the first prediction strategy is derived by accounting timeand event dependent occupancy of the plurality of users 128 associatedwith the built environment 102. In another embodiment of the presentdisclosure, the first prediction strategy is derived by accountingfrequency of usage of each of the plurality of energy consuming devices104 associated with the built environment 102. In yet another embodimentof the present disclosure, the first prediction strategy is derivedaccounting a deviation in load demand from standard demand values.

The energy demand control system 122 derives a second predictionstrategy of the set of prediction strategies. In an embodiment of thepresent disclosure, the second prediction strategy is derived byaccounting key features of architectural designs of the builtenvironment 102. In another embodiment of the present disclosure, thesecond prediction strategy is derived by accounting luminance factorsassociated with the built environment 102. In yet another embodiment ofthe present disclosure, the second strategy is derived by accounting anumber of loads per specific area of the built environment 102.

The energy demand control system 122 derives a third prediction strategyof the set of prediction strategies. In an embodiment of the presentdisclosure, the third prediction strategy is derived by accounting eachuser's associated usage of the plurality of energy consuming devices104. In another embodiment of the present disclosure, the third strategyis derived by accounting pre-collected energy demand profiles. In yetanother embodiment of the present disclosure, the third strategy isderived by accounting a plurality activities associated with each userof the plurality of users 128.

The energy demand control system 122 monitors a set of factorscorresponding to the set of prediction strategies. The set of factorsinclude time and event dependent occupancy factors and frequency ofusage factors of each of the plurality of energy consuming devices 104.In addition, the set of factors include the key architectural designfeature factors and environmental condition factors associated with thebuilt environment 102. Moreover, the set of factors include the energyusage profile factors of a user and load per specific area factorsassociated with the built environment 102. The set of factorscorresponding to the set of prediction strategies discretely indicatesprominence of a type of prediction strategy over another type ofprediction strategy of the set of prediction strategies.

The energy demand control system 122 performs switching to a predictionstrategy from the set prediction strategies. The energy demand controlsystem 122 performs switching based on selective prominence of one ormore factors in the monitored set of factors corresponding to a typeprediction strategy of the set of prediction strategies. In an example,the occupancy pattern of the plurality of users 128 in gymkhana Xincreases during the time interval of 5:00 pm to 10:00 pm. In addition,the occupancy pattern associated with the gymkhana X remains low. Theenergy demand control system 122 associated with the gymkhana X analyzesthat the architectural designs and the average energy consumption ofeach user associated with the gym remains unaltered. In addition, theenergy demand control system 122 takes into account the time dependentoccupancy pattern as a prominent factor for predicting the futurepotential energy demand and consumption associated with the gymkhana X.In addition, the energy demand control system 122 derives the firstprediction strategy.

In another example, the key architectural design features of an assemblyunit associated with an automobile manufacturing plant Y is differentfrom the key architectural design features of a quality testing unitassociated with the automobile manufacturing plant Y. The energy demandcontrol system 122 monitors and analyzes that the key architecturaldesign features associated with both the assembly unit and the qualitytesting unit are crucial in performing operations. In addition, theenergy demand control system 122 takes into account the keyarchitectural design features as a prominent factor for predicting thefuture potential energy demand and consumption. Moreover, the energydemand control system 122 associated with the automobile manufacturingplant Y derives different prediction strategies for the assembly unitand the quality testing unit of the automobile manufacturing plant Y.

In yet another example of a hospital Z, the energy usage profile of aperson D admitted in an ICU ward is monitored by various types of healthmonitoring devices like medical imaging machines, CT scanners, dialysismachines and medical monitors. The energy demand control system 122associated with the hospital Z monitors the real time energy demand andconsumption. In addition, the energy demand control system 122 analyzesand identify that the energy usage profile of a person D is crucial inpredicting the future potential energy demand and consumption associatedwith the ICU ward. The energy demand control system 122 takes intoaccount the energy usage profile of the person D as a prominent factorfor predicting the future potential energy demand and consumptionassociated with the ICU ward. In addition, the energy demand controlsystem 122 performs switching to a type of prediction strategyaccounting the person D's energy usage profile as a prominent factor forpredicting the future potential demand and consumption.

Further, the energy demand control system 122 predicts the set ofpredictions for identifying the potential future time-variant energydemand and consumption associated with the built environment 102. Theenergy demand control system 122 predicts the set of predictions basedon switching the prediction strategies relevant to the time-variantenergy demand and consumptions associated with the built environment102. The energy demand control system 122 predicts the set ofpredictions by performing one or more mathematical functions. In anexample, the one or more mathematical functions include but may not belimited to auto-regressive integrated moving average (hereinafter as“ARIMA”) models and artificial neural networks. In another example, theone or more mathematical functions include but may not be limited toGaussian processes, historical averages, trend analysis andextrapolating functions.

The set of predictions include a correlation of the current energydemand and the past energy demand based on the operational state of eachof the plurality of energy consuming devices 104 associated with thebuilt environment 102. In addition, the set of predictions include thecorrelation of the current energy demand and the past energy demandbased on the energy storage capacity of the plurality of energy storageand supply means 106. Moreover, the set of predictions include thecorrelation of the current energy demand and the past energy demandbased on the environmental sensors recordings and energy pricing. Also,the set of predictions include a recognition of unusual and unexpectedbehaviors in the energy demand and consumption of each of the pluralityof energy consuming devices 104 associated with the built environment102. In addition, the set of predictions include the correlation ofenergy demand and energy storage pertaining to the one or more builtenvironments.

Further, the energy demand control system 122 executes the one or morecontrol schemes to optimize the time-variant energy demand andconsumption of the built environment 102. The energy demand controlsystem 122 executes the one or more control schemes based on the set ofpredictions identifying the potential future time-variant energy demandand consumption associated with the built environment 102. The one ormore control schemes includes a potential operational andnon-operational instructions for optimizing the operating state of theplurality of energy consuming devices 104. In addition, the one or morecontrol schemes includes the potential operational and non-operationalinstructions for improving the energy storage capacity of the pluralityof energy storage and supply means 106. The potential operational andnon-operational instructions include regulating power supply of each ofthe plurality of energy consuming devices 104 based on an occupancypattern, energy demand and architectural design of the built environment102. In addition, the potential operational and non-operationalinstructions include regulating energy consumption duration of theplurality of energy consuming devices 104.

Further, the potential operational and non-operational instructionsinclude notifying a list of malfunctioning devices of the plurality ofenergy consuming devices 104. Furthermore, the potential operational andnon-operational instructions include performing an operation on theplurality of energy consuming devices 104. The operation is selectedfrom a group of operations consisting of upgrading, downgrading,replacing and repairing of the plurality of energy consuming devices104. Moreover, the operational and non-operational instructions includeprompting the plurality of energy storage and supply means 106 to startand stop charge cycles at specific time periods for reducing energyconsumption costs. In addition, the operational and non-operationalinstructions include prompting the plurality of energy storage andsupply means 106 to start and stop discharge cycles for controlling thepeak loading periods. Moreover, the operational and non-operationalinstructions include regulating the charging and dischargingcharacteristics of each of the plurality of energy storage and supplymeans 106.

The energy demand control system 122 executes the one or more controlschemes through the communication network 116. In an embodiment, theenergy demand control system 122 executes the one or more controlschemes through the network based automatic control system 202. Thenetwork based automatic control system 202 is associated with the builtenvironment 102. In addition, the network based automatic control system202 is associated with a plurality of electrical control relays. Inaddition, the network based automatic control system 202 is associatedwith a microprocessor based switches. The network based automaticcontrol system 202 sends one or more control signals based on the one ormore control schemes. The network based automatic control system 202automatically applies the one or more control schemes to the builtenvironment 102. The network based automatic control system 202 controlsthe operation of each of the plurality of energy consuming devices 104.In addition, the network based automatic control system 202 controls theplurality of energy consuming devices 104 based on the occupancybehavior of the plurality of users 128 and energy storage capacity ofthe plurality of energy storage and supply means 106. Moreover, thenetwork based automatic control system 202 controls the plurality ofenergy consuming devices 104 based on weather conditions and real timeenergy pricing associated with the built environment 102. Furthermore,the network based automatic control system 202 controls the plurality ofenergy storage and supply means 106 based on the real time energydemand, weather conditions and forecasts, and real time energy pricingassociated with the built environment 102.

Further, the energy demand control system 122 provides the improvementin the prediction of the potential future time-variant energy demand andconsumption associated with the built environment 102. The improvementin the prediction is obtained from a learning algorithm. The learningalgorithm enable the energy demand control system 122 to predict moreaccurate and precise future time-variant energy demand and consumptionby deriving a specific prediction strategy. In addition, the accurateand precise prediction enables the energy demand control system 122 toexecute the one or more control schemes to control and optimize the timevariant energy demand and consumption in real time.

The learning algorithm accelerates assessment and the analysis of one ormore data points. The one or more data points are associated with theenergy consumption of each of the plurality of energy consuming devices104 and each of the plurality of energy storage and supply means 106installed the built environment 102. The energy demand control system122 utilizes the one or more data points to create a continuous closedcontrol and feedback loop for optimizing the operating state of theplurality of energy consuming devices 104. In addition, the energydemand control system 122 utilizes the one or more data points to createa continuous closed control and feedback loop for improving the energystorage capacity of the plurality of energy storage and supply means106.

The energy demand control system 122 stores the first set of statisticaldata, the second set of statistical data, the third set of statisticaldata, the fourth set of statistical data and the fifth set ofstatistical data in the database 204 a in real time. In addition, theenergy demand control system 122 stores the plurality of statisticalresults and a first log file having the set of prediction strategies inthe database 204 a in real time. Moreover, the energy demand controlsystem 122 stores a second log file having the set of predictions and athird log file having the one or more control schemes in a database 204a in real time.

The energy demand control system 122 updates the first set ofstatistical data, the second set of statistical data, the third set ofstatistical data, the fourth set of statistical data and the fifth setof statistical data. In addition, the energy demand control system 122updates the plurality of statistical results and the first log filehaving the set of prediction strategies in real time. Moreover, theenergy demand control system 122 updates the second log file having theset of predictions and the third log file having the one or more controlschemes in real time.

The energy demand control system 122 displays the first set ofstatistical data, the second set of statistical data, the third set ofstatistical data, the fourth set of statistical data and the fifth setof statistical data on the one or more statistical monitoring devices126. In addition, the energy demand control system 122 displays theplurality of statistical results and the first log file having the setof prediction strategies in real time. Moreover, the energy demandcontrol system 122 stores the second log file having the set ofpredictions and the third log file having the one or more controlschemes in real time.

FIG. 3 illustrates a block diagram 300 of the energy demand controlsystem 122, in accordance with various embodiment of the presentdisclosure. It may be noted that to explain the system elements of FIG.3, references will be made to the system elements of the FIG. 1 and theFIG. 2. The energy demand control system 122 includes a collectionmodule 302, a fetching module 304, an accumulation module 306, areception module 308, a gathering module 310 and an analyzing module312. In addition, the energy demand control system 122 includes aderiving module 314, a monitoring module 316, a switching module 318, aprediction module 320 and an execution module 322. Moreover, the energydemand control system 122 includes a storage module 324, an updatingmodule 326 and a displaying module 328. The above mentioned modules areconfigured for adaptively switching the prediction strategies tooptimize the time-variant energy demand and consumption of the builtenvironment 102.

The collection module 302 collects the first set of statistical dataassociated with each of the plurality of energy consuming devices 104installed in the built environment 102. The first set of statisticaldata includes the current operational state data and the pastoperational state data associated with the plurality of energy consumingdevices 104. The plurality of energy metering devices collects the firstset of statistical data. The plurality of energy metering devicestransfers the first set of statistical data to the one or more datacollecting devices 110. The one or more data collecting devices 110transfer the first set of statistical data to the energy demand controlsystem 122 (as explained above in the detailed description of FIG. 1 andFIG. 2).

The fetching module 304 fetches the second set of statistical dataassociated with the occupancy behavior of the plurality of users 128present inside the built environment 102. The second set of statisticaldata includes the energy consumption behavior of each of the pluralityof users 128 present inside the built environment 102. In addition, thesecond set of statistical data includes the occupancy pattern of each ofthe plurality of users 128 present inside the built environment 102. Theplurality of occupancy detection means and the plurality of sensors 108fetches the second set of statistical data in real time. In addition,the plurality of occupancy detection means and the plurality of sensors108 transfer the second set of statistical data to the energy demandcontrol system 122 (as discussed above in detailed description of FIG. 1and FIG. 2).

The accumulation module 306 accumulates the third set of statisticaldata associated with each of the plurality of energy storage and supplymeans 106 associated with the built environment 102. The third set ofstatistical data includes the current and historical energy storage andsupply capacity data associated with the plurality of energy storage andsupply means 106. The plurality of energy monitoring devices accumulatesthe energy storage and supply capacity data associated with each of theplurality of energy storage and supply means 106 in real time to obtainthe third set of statistical data. In addition, the plurality of energymonitoring devices transfer the third set of statistical data to theenergy demand control system 122 (as explained above in detaileddescription of FIG. 1 and FIG. 2).

The reception module 308 receives the fourth set of statistical dataassociated with each of the plurality of environmental sensors 118. Thefourth set of statistical data includes the current and historicalenvironmental condition data of at least one of inside and outside ofthe built environment 102. The plurality of environmental sensors 118records the environmental condition data in real time to obtain thefourth set of statistical data. In addition, the plurality ofenvironmental sensors 118 transfers the fourth set of statistical datato the energy demand control system 122. Moreover, the reception module308 receives the fourth set of statistical data from the plurality ofexternal APIs 124 and the third party databases 206 (as discussed abovein detailed description of FIG. 1 and FIG. 2).

The gathering module 310 gathers the fifth set of statistical dataassociated with each of the plurality of energy pricing models 120. Thefifth set of statistical data includes the current and historicalrecordings of the energy pricing state affecting the built environment102. The plurality of energy pricing models 120 record the real timeenergy pricing state associated with the built environment 102 to obtainthe fifth set of statistical data. In addition, the plurality of energypricing models transfer the fifth set of statistical data to the energydemand control system 122. Moreover, the gathering module 310 gathersthe fifth set of statistical data from the plurality of external APIs124 and the third party databases 206 (as explained above in detaileddescription of FIG. 1 and FIG. 2).

The analyzing module 312 analyzes the first set of statistical data, thesecond set of statistical data, the third set of statistical data, thefourth set of statistical data and the fifth set of statistical data.The analyzing module 312 includes a translation module 312 a, a parsingmodule 312 b, an imputing module 312 c and a comparison module 312 d.The translation module 312 a translates the current operational statedata and the past operational state data associated with the pluralityof energy consuming devices 104 into the energy demand values. Inaddition, the translation module 312 a translates the currentoperational state data and the past operational state data into theenergy demand values for the pre-defined interval of time (as discussedin detailed description of FIG. 1 and FIG. 2).

Further, the parsing module 312 b parses the first set of statisticaldata, the second set of statistical data and the third set ofstatistical data. The parsing module 312 b parses the first set ofstatistical data, the second set of statistical data and the third setof statistical data based on the physical location of each of theplurality of energy consuming devices 104. In addition, the parsingmodule 312 b parses the first set of statistical data, the second set ofstatistical data and the third set of statistical data based on theoccupancy pattern of the plurality of users 128. Moreover, the parsingmodule 213 b parses the first set of statistical data, the second set ofstatistical data and the third set of statistical data based on theweather conditions and real time energy pricing state (as explainedabove in the detailed description of FIG. 1 and FIG. 2).

Further, the imputing module 312 c imputes the one or more data entriesin the first set of statistical data, the second set of statistical dataand the third set of statistical data (as discussed above in detaileddescription of FIG. 1 and FIG. 2). Furthermore, the comparison module312 d compares the current operational state data with the pastoperational state data associated with the each of the plurality ofenergy consuming devices 104. In addition, the comparison module 312 dcompares the current energy storage capacity and the past energy storagecapacity associated with each of the plurality of energy storage andsupply means 106 (as discussed above in detailed description of FIG. 1and FIG. 2).

The analysis is performed to generate the plurality of statisticalresults associated with the energy consumption of the built environment102 in real time. The plurality of statistical results includes one ormore graphs, one or more charts, one or more tables and one or morestatistical maps of the energy consumption as a function of duration ofthe operations. Further, the plurality of statistical results includesbase-load, variable load, the cost of the operations, energy efficiency,the temperature, humidity and daylight. Furthermore, the plurality ofstatistical results includes the real time occupancy of the plurality ofusers 128 inside the built environment 102 and physical parameters ofeach of the plurality of energy consuming devices 104.

The deriving module 314 derives the set of prediction strategies. Theset of prediction strategies are utilized for identifying the futuretime-variant energy demand and consumption associated with the builtenvironment 102. The energy demand control system 122 derives the set ofprediction strategies based on the plurality of statistical results. Theset of prediction strategies are derived for driving controlled anddirectional execution of analysis of the set of predictions for optimumusage and operation of the plurality of energy consuming devices 104. Inaddition, the set of prediction strategies are derived for drivingcontrolled and directional execution of the one or more control schemesfor optimum usage and operation of the plurality of energy consumingdevices 104. Further, the deriving module 314 derives the firststrategy, the second strategy and the third strategy (as explained indetailed description of FIG. 2).

The monitoring module 316 monitors the set of factors corresponding tothe set of prediction strategies. The set of factors includes time andevent dependent occupancy factors and frequency of usage factors of eachof the plurality of energy consuming devices 104. In addition, the setof factors include the key architectural design feature factors andenvironmental condition factors associated with the built environment102. Moreover, the set of factors include the energy usage profilefactors of a user and load per specific area factors associated with thebuilt environment 102. The set of factors corresponding to the set ofprediction strategies discretely indicates prominence of a type ofprediction strategy over another type of prediction strategy of the setof prediction strategies. Furthermore, the switching module 318 performsswitching to the prediction strategy from the set prediction strategies.The switching module 318 performs switching based on the selectiveprominence of the one or more factors in the monitored set of factors(as discussed in detailed description of FIG. 2).

The prediction module 320 predicts the set of predictions foridentifying the potential future time-variant energy demand andconsumption associated with the built environment 102. The predictionmodule 320 predicts the set of predictions based on switching theprediction strategies relevant to the time-variant energy demand andconsumptions associated with the built environment 102. The predictionmodule 320 predicts the set of predictions by performing the one or moremathematical functions (as mentioned in detailed description of FIG. 2).

Further, the execution module 322 executes the one or more controlschemes to optimize the time-variant energy demand and consumption ofthe built environment 102. The execution module 322 executes the one ormore control schemes based on the set of predictions identifying thepotential future time-variant energy demand and consumption associatedwith the built environment 102. The one or more control schemes includesthe potential operational and non-operational instructions foroptimizing the operating state of the plurality of energy consumingdevices 104.

The storage module 324 stores the first set of statistical data, thesecond set of statistical data, the third set of statistical data, thefourth set of statistical data and the fifth set of statistical data inthe database 204 a in real time. In addition, the storage module 324stores the plurality of statistical results and the first log filehaving the set of prediction strategies in the database 204 a in realtime. Moreover, the storage module 324 stores the second log file havingthe set of predictions and the third log file having the one or morecontrol schemes in a database 204 a in real time.

The updating module 326 updates the first set of statistical data, thesecond set of statistical data, the third set of statistical data, thefourth set of statistical data and the fifth set of statistical data. Inaddition, the updating module 326 updates the plurality of statisticalresults and the first log file having the set of prediction strategiesin real time. Moreover, the updating module 326 updates the second logfile having the set of predictions and the third log file having the oneor more control schemes in real time.

The displaying module 328 displays the first set of statistical data,the second set of statistical data, the third set of statistical data,the fourth set of statistical data and the fifth set of statistical dataon the one or more statistical monitoring devices 126. In addition, thedisplaying module 328 displays the plurality of statistical results andthe first log file having the set of prediction strategies in real time.Moreover, the displaying module 328 stores the second log file havingthe set of predictions and the third log file having the one or morecontrol schemes in real time.

FIG. 4 illustrates a flow chart 400 for adaptively switching theprediction strategies to optimize the time-variant energy demand andconsumption of the built environment 102, in accordance with variousembodiments of the present disclosure. It may be noted that to explainthe process steps of flowchart 400, references will be made to thesystem elements of FIG. 1, FIG. 2 and FIG. 3. It may also be noted thatthe flowchart 400 may have lesser or more number of steps.

The flowchart 400 initiates at step 402. Following step 402, at step404, the analyzing module 312 analyzes the first set of statisticaldata, the second set of statistical data, the third set of statisticaldata, the fourth set of statistical data and the fifth set ofstatistical data by performing the one or more statistical functions.Further at step 406, the deriving module 314 derives the set ofprediction strategies. The set of prediction strategies for drivingcontrolled and directional execution of the analysis and evaluation ofthe set of predictions for optimum usage and operation of the pluralityof energy consuming devices 104. At step 408, the monitoring module 316monitors the set of factors corresponding to the set of predictionstrategies. Further at step 410, the switching module 318 performsswitching to a prediction strategy from the set of derived predictionstrategies. The switching is performed based on the selective prominenceof the one or more factors in the monitored set of factors. At step 412,the prediction module 320 predicts the set of predictions. Theprediction is performed for identifying the potential futuretime-variant energy demand and consumption associated with the builtenvironment 102. Further at step 414, the execution module 322 executesthe one or more control schemes. The one or more control schemes areexecuted based on the set of predictions identifying the potentialfuture time-variant energy demand and consumption associated with thebuilt environment 102. The flow chart 400 terminates at step 416.

FIG. 5 illustrates a block diagram of a computing device 500, inaccordance with various embodiments of the present disclosure. Thecomputing device 500 includes a bus 502 that directly or indirectlycouples the following devices: memory 504, one or more processors 506,one or more presentation components 508, one or more input/output (I/O)ports 510, one or more input/output components 512, and an illustrativepower supply 514. The bus 502 represents what may be one or more busses(such as an address bus, data bus, or combination thereof). Although thevarious blocks of FIG. 5 are shown with lines for the sake of clarity,in reality, delineating various components is not so clear, andmetaphorically, the lines would more accurately be grey and fuzzy. Forexample, one may consider a presentation component such as a displaydevice to be an I/O component. Also, processors have memory. Theinventors recognize that such is the nature of the art, and reiteratethat the diagram of FIG. 5 is merely illustrative of an exemplarycomputing device 500 that can be used in connection with one or moreembodiments of the present invention. Distinction is not made betweensuch categories as “workstation,” “server,” “laptop,” “hand-helddevice,” etc., as all are contemplated within the scope of FIG. 5 andreference to “computing device.” The computing device 500 typicallyincludes a variety of computer-readable media. The computer-readablemedia can be any available media that can be accessed by the computingdevice 500 and includes both volatile and nonvolatile media, removableand non-removable media. By way of example, and not limitation, thecomputer-readable media may comprise computer storage media andcommunication media. The computer storage media includes volatile andnonvolatile, removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules or other data. Thecomputer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by the computing device 500. The communicationmedia typically embodies computer-readable instructions, datastructures, program modules or other data in a modulated data signalsuch as a carrier wave or other transport mechanism and includes anyinformation delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of any ofthe above should also be included within the scope of computer-readablemedia.

Memory 504 includes computer-storage media in the form of volatileand/or nonvolatile memory. The memory 504 may be removable,non-removable, or a combination thereof. Exemplary hardware devicesinclude solid-state memory, hard drives, optical-disc drives, etc. Thecomputing device 500 includes one or more processors that read data fromvarious entities such as memory 504 or I/O components 512. The one ormore presentation components 508 present data indications to a user orother device. Exemplary presentation components include a displaydevice, speaker, printing component, vibrating component, etc. The oneor more I/O ports 510 allow the computing device 500 to be logicallycoupled to other devices including the one or more I/O components 512,some of which may be built in. Illustrative components include amicrophone, joystick, game pad, satellite dish, scanner, printer,wireless device, etc.

The present disclosure has many advantages over the existing art. Thepresent disclosure provides technical advantages, economic advantages aswell as ancillary benefits. The present disclosure enables theutilization of the energy storage and supply means having relativelysmaller size and energy storage capacity for addressing the sametime-variant load reduction requirements. In addition, the presentdisclosure controls a large amount of energy loads or demands and costassociated with the installation and operations. Moreover, the presentdisclosure provides a stable grid network resulting in stable energypricing and removing volatility of current power system designs.

The foregoing descriptions of specific embodiments of the presenttechnology have been presented for purposes of illustration anddescription. They are not intended to be exhaustive or to limit thepresent technology to the precise forms disclosed, and obviously manymodifications and variations are possible in light of the aboveteaching. The embodiments were chosen and described in order to bestexplain the principles of the present technology and its practicalapplication, to thereby enable others skilled in the art to best utilizethe present technology and various embodiments with variousmodifications as are suited to the particular use contemplated. It isunderstood that various omissions and substitutions of equivalents arecontemplated as circumstance may suggest or render expedient, but suchare intended to cover the application or implementation withoutdeparting from the spirit or scope of the claims of the presenttechnology.

While several possible embodiments of the invention have been describedabove and illustrated in some cases, it should be interpreted andunderstood as to have been presented only by way of illustration andexample, but not by limitation. Thus, the breadth and scope of apreferred embodiment should not be limited by any of the above-describedexemplary embodiments.

What is claimed is:
 1. A computer-implemented method for adaptivelyswitching prediction strategies to optimize time-variant energy demandand consumption of one or more built environments, thecomputer-implemented method comprising analyzing, at an energy demandcontrol system with a processor, a first set of statistical dataassociated with a plurality of energy consuming devices, a second set ofstatistical data associated with an occupancy behavior of a plurality ofusers, a third set of statistical data associated with a plurality ofenergy storage and supply means, a fourth set of statistical dataassociated with a plurality of environmental sensors and a fifth set ofstatistical data associated with a plurality of energy pricing modelsincluding current and historical recordings of energy pricing stateaffecting power purchases at the one or more built environments, theanalyzing being done by performing one or more statistical functions togenerate a plurality of statistical results; deriving, at the energydemand control system with the processor, a set of prediction strategiesbased on the plurality of statistical results, the set of predictionstrategies driving controlled and directional execution of analysis andevaluation of a set of predictions for optimum usage and operation ofthe plurality of energy consuming devices; monitoring, at the energydemand control system with the processor, a set of factors correspondingto the set of prediction strategies discretely indicating prominence ofa type of prediction strategy over an another type of predictionstrategy of the set of prediction strategies, wherein the monitoringbeing performed in real time; switching, at the energy demand controlsystem with the processor, to a prediction strategy from the set ofderived prediction strategies, wherein the switching being performedbased on selective prominence of one or more factors in the monitoredset of factors corresponding to switched predication strategy of the oneor more prediction strategies and wherein the switching being performedin real time; predicting, at the energy demand control system with theprocessor, a set of predictions for identifying a potential futuretime-variant energy demand and consumption associated with the one ormore built environments, wherein the prediction being performed in realtime; and executing, at the energy demand control system with theprocessor, one or more control schemes for controlling the time-variantenergy demand and consumption of the one or more built environmentsassociated with the renewable energy sources, the one or more controlschemes being executed based on the set of predictions identifying thepotential future time-variant energy demand and consumption associatedwith the one or more built environments.
 2. The computer-implementedmethod of claim 1, wherein the set of prediction strategies comprises: afirst prediction strategy of accounting time and event dependentoccupancy, frequency of usage of each of the plurality of energyconsuming devices and deviation in load demand from standard demandvalues; a second prediction strategy of accounting key features ofarchitectural design of the one or more built environments, luminancefactors and number of loads per specific area of the one or more builtenvironments; and a third prediction strategy of accounting each user'sassociated usage of the plurality of energy consuming devices,pre-collected energy demand profiles and a plurality of activities ofeach user of the plurality of users.
 3. The computer-implemented methodof claim 1, wherein the set of predictions comprises a correlation of acurrent energy demand and a past energy demand based on the operationalstate of the plurality of energy consuming devices, energy storagecapacity of the plurality of energy storage and supply means,environmental sensors recordings and energy pricing, a recognition ofunusual and unexpected behaviors in the energy demand and consumption ofeach of the plurality of energy consuming devices, correlation of energydemand and energy storage pertaining to the one or more builtenvironments.
 4. The computer-implemented method of claim 1, furthercomprising collecting, at the energy demand control system with theprocessor, the first set of statistical data associated with theplurality of energy consuming devices present in the one or more builtenvironments, wherein the first set of statistical data comprises acurrent operational state data associated with the energy consumingdevices and a past operational state data associated with the energyconsuming devices, wherein the first set of statistical data beingcollected based on a first plurality of parameters, wherein the firstplurality of parameters comprises a set of operational characteristicsassociated with each of the plurality of energy consuming devices and aset of physical characteristics associated with each of the plurality ofenergy consuming devices, wherein the set of operational characteristicscomprises a current rating, a voltage rating, a power rating, afrequency of operation, an operating temperature, a device temperature,the duration of operation, a seasonal variation in operation andoff-seasonal variation in operation and wherein the set of physicalcharacteristics comprises a device size, a device area, a devicephysical location and a portability of device and wherein the first setof statistical data being collected in real time.
 5. Thecomputer-implemented method of claim 1, further comprising fetching, atthe energy demand control system with the processor, the second set ofstatistical data associated with the occupancy behavior of the pluralityof users present inside each of the one or more built environments,wherein the second set of statistical data comprises a first pluralityof occupancy data and a second plurality of occupancy data, wherein thefirst plurality of occupancy data being associated with energyconsumption behavior of each of one or more occupants present inside theone or more built environments and the second plurality of occupancydata being associated with an occupancy pattern of each of the one ormore occupants present inside the one or more built environments.
 6. Thecomputer-implemented method of claim 1, further comprising accumulating,at the energy demand control system with the processor, the third set ofstatistical data associated with each of the plurality of energy storageand supply means, wherein the third set of statistical data comprises acurrent and historical energy storage and supply capacity dataassociated with the plurality of energy storage and supply means,wherein the accumulation of the third set of statistical data beingperformed based on a third plurality of parameters, wherein the thirdplurality of parameters comprises charging and discharging rates,temperature characteristics, an energy storage and release capacity,charge current, charge level, discharge current, idle time and depth ofdischarge associated with the plurality of energy storage and supplymeans and wherein the third set of statistical data being accumulated inreal time.
 7. The computer-implemented method of claim 1, furthercomprising receiving, at the energy demand control system with theprocessor, the fourth set of statistical data associated with each ofthe plurality of environmental sensors pertaining to the one or morebuilt environments, wherein the fourth set of statistical data comprisesa current and historical environmental condition data associated withthe one or more built environments, wherein the reception of the fourthset of statistical data being performed based on a fourth plurality ofparameters, wherein the fourth plurality of parameters comprises a meansof recording environmental data comprising temperature, humidity and airpressure associated with each of the plurality of environmental sensorsassociated with the one or more built environments and wherein theenvironmental data being obtained from a plurality of externalapplication programming interfaces and a plurality of third partydatabases and wherein the fourth set of statistical data being receivedin real time.
 8. The computer-implemented method of claim 1, furthercomprising gathering, at the energy demand control system with theprocessor, the fifth set of statistical data associated with each of theplurality of energy pricing models, the gathering of the fifth set ofstatistical data being done based on a fifth plurality of parameters,wherein the fifth plurality of parameters comprises a means of recordingenergy pricing data including an energy pricing model or an energy pricesignal associated with the one or more built environments and whereinthe energy pricing data is obtained from a plurality of externalapplication programming interfaces and a plurality of third partydatabases and wherein the fifth set of statistical data is gathered inreal time.
 9. The computer-implemented method of claim 1, wherein theone or more control schemes comprises potential operational andnon-operational instructions and wherein the potential operational andnon-operational instructions comprises: regulating power supply of eachof the plurality of energy consuming devices based on an occupancypattern, energy demand and architectural design of the one or more builtenvironments; regulating energy consumption duration of the plurality ofenergy consuming devices; performing an operation on the plurality ofenergy consuming devices, the operation being selected from a group ofoperations consisting of upgrading, downgrading, replacing and repairingof the plurality of energy consuming devices; prompting the plurality ofenergy storage and supply means to start and stop charge cycles atspecific time periods for reducing energy consumption costs; promptingthe plurality of energy storage and supply means to start and stopdischarge cycles for controlling peak loading periods; and regulatingcharging and discharging characteristics of each of the plurality ofenergy storage and supply means.
 10. The computer-implemented method ofclaim 1, wherein the one or more statistical functions comprises:translating current operational state data and past operational statedata associated with the plurality of energy consuming devices intoenergy demand values for a pre-defined interval of time; parsing thefirst set of statistical data, the second set of statistical data andthe third set of statistical data; imputing one or more data entries inthe first set of statistical data, the second set of statistical dataand the third set of statistical data based on a self-learningalgorithm; and comparing the current operational state data with thepast operational state data to determine potential for improvement inenergy consumption of each of the plurality of energy consuming devicesand a current energy storage capacity and a past energy storage capacityassociated with each of the plurality of energy storage and supplymeans.
 11. The computer-implemented method of claim 1, wherein theplurality of statistical results comprises one or more graphs, one ormore charts, one or more tables and one or more statistical maps ofenergy consumption as a function of duration of the operations of theplurality of energy consuming devices, energy storage and supplycapacity of the plurality of energy storage and supply means,environmental conditions and energy pricing affecting the one or morebuilt environments.
 12. The computer-implemented method of claim 1,further comprising storing, at the energy demand control system with theprocessor, the first set of statistical data, the second set ofstatistical data, the third set of statistical data, the fourth set ofstatistical data, the fifth set of statistical data, the plurality ofstatistical results, a first log file having the set of predictionstrategies, a second log file having the set of predictions and a thirdlog file having the one or more control schemes in a database, whereinthe storing being done in real time.
 13. The computer-implemented methodof claim 1, further comprising updating, at the energy demand controlsystem with the processor, the first set of statistical data, the secondset of statistical data, the third set of statistical data, the fourthset of statistical data, the fifth set of statistical data, theplurality of statistical results, a first log file having the set ofprediction strategies, a second log file having the set of predictionsand a third log file having the one or more control schemes, wherein theupdating being done in real time.
 14. The computer-implemented method ofclaim 1, further comprising displaying, at the energy demand controlsystem with the processor, the first set of statistical data, the secondset of statistical data, the third set of statistical data, the fourthset of statistical data, the fifth set of statistical data, theplurality of statistical result, a first log file having the set ofprediction strategies, a second log file having the set of predictionsand a third log file having the one or more control schemes, wherein thedisplaying being provided on one or more statistical monitoring devicesin real time.
 15. A computer system comprising one or more processors;and a memory coupled to the one or more processors, the memory forstoring instructions which, when executed by the one or more processors,cause the one or more processors to perform a method for adaptivelyswitching prediction strategies to optimize time-variant energy demandand consumption of one or more built environments associated withrenewable energy sources, the method comprising: analyzing, at an energydemand control system, a first set of statistical data associated with aplurality of energy consuming devices, a second set of statistical dataassociated with an occupancy behavior of a plurality of users, a thirdset of statistical data associated with a plurality of energy storageand supply means, a fourth set of statistical data associated with aplurality of environmental sensors and a fifth set of statistical dataassociated with a plurality of energy pricing models including currentand historical recordings of energy pricing state affecting powerpurchase at the one ft more built environments, the analyzing beingperformed for determining optimized operating states of the plurality ofenergy consuming devices, the analyzing being done by performing one ormore statistical functions to generate a plurality of statisticalresults; deriving, at the energy demand control system, a set ofprediction strategies based on the plurality of statistical results, theset of prediction strategies driving controlled and directionalexecution of analysis and evaluation of a set of predictions for optimumusage and operation of the plurality of energy consuming devices;monitoring, at the energy demand control system, a set of factorscorresponding to the set of prediction strategies discretely indicatingprominence of the set of prediction strategies; switching, at the energydemand control system, to a prediction strategy from the set of derivedprediction strategies, wherein the switching being performed based onselective prominence of one or more factors in the monitored set offactors corresponding switched predication strategy of one or moreprediction strategies; predicting, at the energy demand control system,the set of predictions for identifying a potential future time-variantenergy demand and consumption associated with the one or more builtenvironments; and executing, at the energy demand control system, one ormore control schemes for controlling the time-variant energy demand andconsumption of the one or more built environments associated with therenewable energy sources, the one or more control schemes being executedbased on the set of predictions identifying the potential futuretime-variant energy demand and consumption associated with the one ormore built environments.
 16. The computer system of claim 15, furthercomprising storing, at the energy demand control system, the first setof statistical data, the second set of statistical data, the third setof statistical data, the fourth set of statistical data, the fifth setof statistical data, the plurality of statistical results, a first logfile having the set of prediction strategies, a second log file havingthe set of predictions and a third log file having the one or morecontrol schemes in a database, wherein the storing being done in realtime.
 17. The computer system of claim 15, further comprising updating,at the energy demand control system, the first set of statistical data,the second set of statistical data, the third set of statistical data,the fourth set of statistical data, the fifth set of statistical data,the plurality of statistical results, a first log file having the set ofprediction strategies, a second log file having the set of predictionsand a third log file having the one or more control schemes, wherein theupdating being done in real time.
 18. A non-transitory computer-readablestorage medium encoding computer executable instructions that, whenexecuted by at least one processor, performs a method for adaptivelyswitching prediction strategies to optimize time-variant energy demandand consumption of one or more built environments, the methodcomprising: analyzing, at a computing device, a first set of statisticaldata associated with a plurality of energy consuming devices, a secondset of statistical data associated with an occupancy behavior of aplurality of users, a third set of statistical data associated with aplurality of energy storage and supply means, a fourth set ofstatistical data associated with a plurality of environmental sensorsand a fifth set of statistical data associated with a plurality ofenergy pricing models including current and historical recordings ofenergy pricing state affecting power purchase at the one or more builtenvironments, the analyzing being performed for determining optimizedoperating states of the plurality of energy consuming devices, theanalyzing being done by performing one or more statistical functions togenerate a plurality of statistical results; deriving, at the computingdevice, a set of prediction strategies based or the plurality ofstatistical results, the set of prediction strategies driving controlledand directional execution of analysis and evaluation of a set ofpredictions for optimum usage and operation of the plurality of energyconsuming devices; monitoring, at the computing device, a set of factorscorresponding to the set of prediction strategies discretely indicatingprominence of the set of prediction strategies; switching, at thecomputing device, to a prediction strategy from the set of derivedprediction strategies, wherein the switching being performed based onselective prominence of one or more factors in the monitored set offactors corresponding switched predication strategy of one or moreprediction strategies; predicting, at the computing device, the set ofpredictions for identifying a potential future time-variant energydemand and consumption associated with the one or more builtenvironments; and executing, at the computing device, one or morecontrol schemes for controlling the time-variant energy demand andconsumption of the one or more built environments associated with therenewable energy sources, wherein the one or more control schemes beingexecuted based on the set of predictions identifying the potentialfuture time-variant energy demand and consumption associated with theone or more built environments.
 19. The non-transitory computer-readablestorage medium of claim 18, further comprising storing, at the computingdevice, the first set of statistical data, the second set of statisticaldata, the third set of statistical data, the fourth set of statisticaldata, the fifth set of statistical data, the plurality of statisticalresults, a first log file having the set of prediction strategies, asecond log file having the set of predictions and a third log filehaving the one or more control schemes in a database, wherein thestoring being done in real time.
 20. The non-transitorycomputer-readable storage medium of claim 18, further comprisingupdating, at the computing device, the first set of statistical data,the second set of statistical data, the third set of statistical data,the fourth set of statistical data, the fifth set of statistical data,the plurality of statistical results, a first log file having the set ofprediction strategies, a second log file having the set of predictionsand a third log file having the one or more control schemes, wherein theupdating being done in real time.